TIGGE research

1. TIGGE research Richard Swinbank GIFS-TIGGE Working Group meeting #9, Aug-Sep 2011

2. TIGGE Research Following the successful establishment of the TIGGE dataset, the main focus of the GIFS-TIGGE working group has shifted towards research on ensemble forecasting. Particular topics of interest include: • a posteriori calibration of ensemble forecasts (bias correction, downscaling, etc.); • combination of ensembles produced by multiple models; • research on and development of probabilistic forecast products. TIGGE data is also invaluable as a resource for a wide range of research projects, for example on dynamical processes and predictability – for example, see presentations in this meeting. Up to the end of 2010, 43 articles related to TIGGE have been published in the scientific literature

3. Multi-model ensemble compared with reforecast calibration • Reforecast calibration gives comparable benefit to multi-model ensemble • Choice of verification data set (in this case, ERA-Interim) could have subtle but significant effect on relative benefits • Calibration could further enhance benefit of multi-model ensemble Renate Hagedorn

4. Uncalibrated precipitation forecasts Probabilistic verification • Based on ECMWF, UKMO, NCEP, 12 hour accumulations, 2 years data (autumn 2007 - autumn 2009) for UK region. • Verified against UKPP composite data; thresholds taken from one-month 5x5 gridpoint ukpp climatologies • Multimodel (pfconcat) has consistent slight advantage over single model ensembles in resolution (solid) and reliability penalty (dotted) • The overall Brier Skill Score (resolution-reliability) is negative for long lead times and high thresholds Single model ensembles Multimodel ensemble Jonathan Flowerdew, Met Office

5. Precipitation forecasts over USA • 24 hour accumulations, data from 1 July 2010 to 31 October 2010. • 20 members each from ECMWF, NCEP, UK Met Office, Canadian Meteorological Centre. • 80-member, equally weighted, multi-model ensemble verified as well. • Verification follows Hamill and Juras (QJ, Oct 2006) to avoid over-estimating skill due to variations in climatology. • Conclusions: • ECMWF generally most skillful. • Multi-model beats all. Tom Hamill

6. Ensemble mean error – Propagation speed Propagation speed bias Comparison of extra-tropical cyclone tracks Ensemble mean error: Position (verified against ECMWF analyses) Lizzie Froude, U. Reading

7. Spatiotemporal Behaviour of TIGGE forecast perturbations V(t)(log) variance Indicates how spatial correlation & localisation vary as perturbations grow. M(t) (log) perturbation amplitude Kipling et al, 2011

8. North Atlantic eddy-driven jet “regimes” • North Atlantic eddy-driven jet profile is taken as vertically/zonally averaged low-level zonal wind in North Atlantic sector (15-75N, 300-360E) • Split into three clusters S, M, N using K-means clustering • Transition probability defined: Tom Frame, John Methven, U. Reading

9. Brier Skill Score: regime transition probabilities 3years of TIGGE data for ONDJF (2007-2010), ECMWF, UKMO, MSC

10. MJO Forecast comparison - ECMWF and UKMO have a superior performance in simulating MJO. - Predicted phase speed tends to be slower than observed one. • Predicted amplitude tends to belarger than observed one. Matsueda and Endo (2011, GRL accepted)

11. Tropical cyclone forecasts – ensemble spread contradictions ECMWF (50 members) NCEP (20 members) Sinlaku initiated at 12UTC 10 Sep. 2008 Japan Black line: Best track Grey lines: Ensemble member Munehiko Yamaguchi Philippines Taiwan Dolphin initiated at 00UTC 13 Dec. 2008

12. ECMWF NCEP Steering vector T+0h Asymmetric propagation vector Spread grows with time Does not spread with time T+48h • SV-based perturbations better capture: • Baroclinic energy conversion within a vortex • Baroclinic energy conversion associated with mid-latitude waves • Barotropic energy conversion within a vortex • Munehiko Yamaguchi

13. Comparisons of TC track forecasts • NOAA developing EnKF for eventual operational use in hybrid EnKF/variational data assimilation system. • Early June 2010 through end of October 2010; verification against “best track” information. • Out-performs NCEP operational - differences are statistically significant. • Also compares well with ECMWF (not shown) 24 Tom Hamill

14. How can we further increase impact of TIGGE on research? • Publicity • New leaflet • Website • How to publicise better to universities? • Scientific publications • Conferences/meetings • THORPEX symposia & regional meetings • Other conference & workshops IAMAS, AMS, EMS, AGU… • Communications • tiggeusers mailing list hardly used • What about social media: facebook, twitter…? • How else?

15. TIGGE – next steps • References on website • Volunteer required • Review Article on TIGGE research • When? • Additional data • Stratospheric Network on Assessment of Predictability (SNAP) – Andrew Charlton. Inviting TIGGE providers to join as partners

16. Research needs and priorities • Current emphasis • Calibration and combination methods • Bias correction, downscaling • Multi-model ensembles; reforecasts • Development of probabilistic forecast products – GIFS development • Tropical cyclones (CXML-based) • Gridded data: heavy precipitation; strong winds • Focus on downstream use of ensembles, rather than on improving EPSs

17. Research needs and priorities • But other important areas for EPSs include • Initial conditions – link with ensemble data assimilation (DAOS) • Representing model error – stochastic physics (PDP, WGNE) • Seamless forecasting – links with sub-seasonal forecasting (new project) • Convective-scale ensembles (TIGGE-LAM, MWFR) • Fragmented approach, across several WGs. • But these areas, particularly first two, are important for improving EPS skill and products.

18. Ensemble Forecasts Evaluate, Diagnose Develop, Improve Virtuous Circle To improve EPSs we need to develop a virtuous circle – best with a single group with focus on ensemble prediction

19. TIGGE development GIFS Products EPS improvement Evolution of TIGGE & GIFS Time • The initial focus of GIFS-TIGGE WG was on establishing the TIGGE database. • We then broadened our scope to include downstream ensemble combination, calibration & product development for GIFS. • We should also use the WG as a forum to discuss R&D focused on improving our EPS systems.